A Novel Hybrid Interval Prediction Approach Based on Modified Lower Upper Bound Estimation in Combination with Multi-Objective Salp Swarm Algorithm for Short-Term Load Forecasting

作     者：Wang, Jiyang Gao, Yuyang Chen, Xuejun 

作者机构：Macau Univ Sci & Technol Fac Informat Technol Macau 999078 Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Gansu Meteorol Serv Ctr Lanzhou 730020 Gansu Peoples R China 

出 版 物：《ENERGIES》 (Energies)

年 卷 期：2018年第11卷第6期

页      面：1561-1561页

核心收录：

基　　金：National Natural Science Foundation of China Gansu science and technology program "Study on the forecasting methods of very short-term wind speeds" [1506RJZA187] 

主　　题：short-term load forecasting interval prediction lower upper bound estimation artificial intelligence multi-objective optimization algorithm data preprocessing 

摘      要：Effective and reliable load forecasting is an important basis for power system planning and operation decisions. Its forecasting accuracy directly affects the safety and economy of the operation of the power system. However, attaining the desired point forecasting accuracy has been regarded as a challenge because of the intrinsic complexity and instability of the power load. Considering the difficulties of accurate point forecasting, interval prediction is able to tolerate increased uncertainty and provide more information for practical operation decisions. In this study, a novel hybrid system for short-term load forecasting (STLF) is proposed by integrating a data preprocessing module, a multi-objective optimization module, and an interval prediction module. In this system, the training process is performed by maximizing the coverage probability and by minimizing the forecasting interval width at the same time. To verify the performance of the proposed hybrid system, half-hourly load data are set as illustrative cases and two experiments are carried out in four states with four quarters in Australia. The simulation results verified the superiority of the proposed technique and the effects of the submodules were analyzed by comparing the outcomes with those of benchmark models. Furthermore, it is proved that the proposed hybrid system is valuable in improving power grid management.